Adjunct Professor of Biostatistics

Research

Many important questions in public health are about the effects of interventions, e.g. changing health policy, approving new drugs or implementing optimal treatment strategies. The answer to these questions often relies on either non-experimental, i.e. observational, data or on imperfect experimental data, i.e. randomized trial data from suffering from non-compliance, drop-outs, intermittent non-response, censoring, etc.

My research is in the development of analytical tools for estimating, from non or imperfect experimental data, the effects of interventions This work falls into the general area of causal inference and missing and censored data analysis.

I am primarily interested in the development of (semiparametric efficient) methods that exploit the information in the available data without making unnecessary assumptions about the parts of the data generating process that are not of substantive interest.